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import os |
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import argparse |
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import gradio as gr |
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from difflib import Differ |
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from functools import partial |
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from string import Template |
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from utils import load_prompt, setup_gemini_client |
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def parse_args(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--ai-studio-api-key", type=str, default=os.getenv("GEMINI_API_KEY")) |
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parser.add_argument("--vertexai", action="store_true", default=False) |
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parser.add_argument("--vertexai-project", type=str, default="gcp-ml-172005") |
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parser.add_argument("--vertexai-location", type=str, default="us-central1") |
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parser.add_argument("--model", type=str, default="gemini-1.5-flash") |
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parser.add_argument("--prompt-tmpl-path", type=str, default="configs/prompts.toml") |
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parser.add_argument("--css-path", type=str, default="statics/styles.css") |
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args = parser.parse_args() |
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return args |
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def find_attached_file(filename, attached_files): |
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for file in attached_files: |
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if file['name'] == filename: |
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return file |
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return None |
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def echo(message, history, state): |
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attached_file = None |
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if message['files']: |
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path_local = message['files'][0] |
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filename = os.path.basename(path_local) |
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attached_file = find_attached_file(filename, state["attached_files"]) |
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if attached_file is None: |
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path_gcp = client.files.upload(path=path_local) |
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state["attached_files"].append({ |
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"name": filename, |
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"path_local": path_local, |
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"gcp_entity": path_gcp, |
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"path_gcp": path_gcp.name, |
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"mime_type=": path_gcp.mime_type, |
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"expiration_time": path_gcp.expiration_time, |
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}) |
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attached_file = path_gcp |
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user_message = [message['text']] |
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if attached_file: user_message.append(attached_file) |
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chat_history = state['messages'] |
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chat_history = chat_history + user_message |
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state['messages'] = chat_history |
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response = client.models.generate_content( |
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model="gemini-1.5-flash", |
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contents=state['messages'] |
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) |
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model_response = response.text |
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if state['summary'] != "": |
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response = client.models.generate_content( |
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model="gemini-1.5-flash", |
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contents=[ |
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Template( |
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prompt_tmpl['summarization']['prompt'] |
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).safe_substitute( |
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previous_summary=state['summary'], |
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latest_conversation=str({"user": message['text'], "assistant": model_response}) |
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) |
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] |
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) |
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if state['summary'] != "": |
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prev_summary = state['summary_history'][-1] |
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else: |
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prev_summary = "" |
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d = Differ() |
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state['summary'] = response.text |
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state['summary_history'].append(response.text) |
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state['summary_diff_history'].append( |
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[ |
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(token[2:], token[0] if token[0] != " " else None) |
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for token in d.compare(prev_summary, state['summary']) |
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] |
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) |
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return ( |
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model_response, |
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state, |
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state['summary_diff_history'][-1], |
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state['summary_history'][-1], |
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gr.Slider( |
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maximum=len(state['summary_history']), |
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value=len(state['summary_history']), |
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visible=False if len(state['summary_history']) == 1 else True, interactive=True |
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), |
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) |
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def change_view_toggle(view_toggle): |
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if view_toggle == "Diff": |
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return ( |
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gr.HighlightedText(visible=True), |
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gr.Markdown(visible=False) |
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) |
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else: |
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return ( |
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gr.HighlightedText(visible=False), |
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gr.Markdown(visible=True) |
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) |
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def navigate_to_summary(summary_num, state): |
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return ( |
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state['summary_diff_history'][summary_num-1], |
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state['summary_history'][summary_num-1] |
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) |
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def main(args): |
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style_css = open(args.css_path, "r").read() |
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global client, prompt_tmpl |
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client = setup_gemini_client(args) |
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prompt_tmpl = load_prompt(args) |
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with gr.Blocks(css=style_css) as demo: |
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state = gr.State({ |
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"messages": [], |
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"attached_files": [], |
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"summary": "", |
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"summary_history": [], |
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"summary_diff_history": [] |
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}) |
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with gr.Column(): |
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gr.Markdown("# Adaptive Summarization") |
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gr.Markdown("AdaptSum stands for Adaptive Summarization. This project focuses on developing an LLM-powered system for dynamic summarization. Instead of generating entirely new summaries with each update, the system intelligently identifies and modifies only the necessary parts of the existing summary. This approach aims to create a more efficient and fluid summarization process within a continuous chat interaction with an LLM.") |
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with gr.Column(): |
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with gr.Accordion("Adaptively Summarized Conversation", elem_id="adaptive-summary-accordion", open=False): |
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with gr.Row(elem_id="view-toggle-btn-container"): |
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view_toggle_btn = gr.Radio( |
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choices=["Diff", "Markdown"], |
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value="Markdown", |
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interactive=True, |
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elem_id="view-toggle-btn" |
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) |
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summary_diff = gr.HighlightedText( |
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label="Summary so far", |
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combine_adjacent=True, |
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show_legend=True, |
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color_map={"+": "red", "-": "green"}, |
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elem_classes=["summary-window"], |
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visible=False |
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) |
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summary_md = gr.Markdown( |
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label="Summary so far", |
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value="No summary yet. As you chat with the assistant, the summary will be updated automatically.", |
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elem_classes=["summary-window"], |
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visible=True |
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) |
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summary_num = gr.Slider(label="summary history", minimum=1, maximum=1, step=1, show_reset_button=False, visible=False) |
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view_toggle_btn.change(change_view_toggle, inputs=[view_toggle_btn], outputs=[summary_diff, summary_md]) |
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summary_num.release(navigate_to_summary, inputs=[summary_num, state], outputs=[summary_diff, summary_md]) |
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with gr.Column("chat-window", elem_id="chat-window"): |
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gr.ChatInterface( |
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multimodal=True, |
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type="messages", |
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fn=echo, |
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additional_inputs=[state], |
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additional_outputs=[state, summary_diff, summary_md, summary_num], |
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) |
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return demo |
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if __name__ == "__main__": |
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args = parse_args() |
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demo = main(args) |
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demo.launch() |
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